Machine Learning Research Scientist, Mechanical Intuition in Multimodal Models

Responsibilities

• Design and implement end-to-end modeling pipelines for machine assembly tasks, building from the ground up rather than adapting existing frameworks
• Run systematic experiments to evaluate architectural variants, data collection and curation strategies, and a range of supervised and reinforcement learning techniques for physical manipulation
• Develop and maintain rigorous evaluation protocols to measure policy performance across assembly scenarios, including generalization to novel parts, configurations, and failure modes
• Explore how modern LLMs and agentic systems can be integrated to support physical reasoning and task planning in assembly contexts
• Collaborate with researchers and engineers across TRI and Toyota's broader ecosystem to connect learning-based systems with real hardware and manufacturing workflows
• Contribute to writing and publishing research results in peer-reviewed venues

Qualifications

• A PhD in a relevant field such as Computer Science, Robotics, Mechanical Engineering, or a related discipline, completed recently (or nearing completion), with some post-PhD or internship work experience
• A demonstrated track record of implementing non-trivial learning systems — not just running baselines, but building pipelines and components from scratch
• Hands-on experience with policy learning, reinforcement learning, or robot learning, with strong intuitions about what makes these approaches succeed or fail in practice
• Proficiency in Python and comfort working across the full stack of a research project, from data processing to model training to evaluation
• Genuine interest in how physical products are designed and manufactured

Bonus Qualifications

• Familiarity with large language models, vision-language models, or agentic AI frameworks, particularly in contexts involving structured reasoning or tool use
• Experience with robot manipulation, motion planning, or sim-to-real transfer
• Experience building or contributing to production-level research codebases

Pay Range

The pay range for this position at commencement of employment is expected to be between $176,000 and $253,000/year for California-based roles, and between $158,400 and $227,700/year for Massachusetts-based roles. Base pay offered will depend on multiple individualized factors, including, but not limited to, a candidate’s experience, skills, job-related knowledge, and market location.

Benefits

TRI offers a generous benefits package including medical, dental, and vision insurance, 401(k) eligibility, paid time off benefits (including vacation, sick time, and parental leave), and an annual cash bonus structure.